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"A Bayesian analysis of microbiome data"

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If you have a question about this talk, please contact Alison Quenault.

Techniques based on sequencing 16S ribosomal DNA have been used for several years to characterise microbial communities. We introduce a Bayesian nonparametric analysis of dependent discrete distributions which can be applied to the analysis of these experiments. The procedure deals effectively with rare species without the need for truncation or rarefaction, and the model makes it possible to infer species co-occurrence patterns that are usually observed in these datasets, unlike other Bayesian approaches such as the Dirichlet-Multinomial model without prior dependence. The dependence between distributions is expressed by latent features, which makes the model especially suited to the joint analysis of multi-omic experiments which might generate, for example, metabolic profiles, RNA expression data, or proteomics data in addition to the species-level characterisation of a 16S sequencing experiment. We describe a simple approach to visualise the posterior uncertainty in ecological ordinations typically applied in microbiome studies.

This talk is part of the MRC Biostatistics Unit Seminars series.

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